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Record W2161044732 · doi:10.1139/w11-129

Diversity of bacteria and mycobacteria in biofilms of two urban drinking water distribution systems

2012· article· en· W2161044732 on OpenAlexvenueno aff
Ruyin Liu, Zhisheng Yu, Hongxun Zhang, Min Yang, Baoyou Shi, Xinchun Liu

Bibliographic record

VenueCanadian Journal of Microbiology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
FundersUniversity of Chinese Academy of Sciences
KeywordsBiofilmMicrobiologyBiologyBacteria16S ribosomal RNAMycobacteriumProteobacteriaPhylotypeGenetics

Abstract

fetched live from OpenAlex

In this study, to give insight into the bacterial diversity of biofilms from full-scale drinking water distribution systems (DWDSs), the bacterial community compositions of biofilms from two urban DWDSs (Guangzhou and Beijing, China) were determined using a 16S rRNA gene library technique. Meanwhile, the occurrence and diversity of mycobacteria were also analyzed by a Mycobacterium -specific hsp gene assay. The biofilms from the full-scale DWDSs have complex bacterial populations. Proteobacteria was the common and predominant group in all biofilm samples, in agreement with previous reports. The community structures of bacteria at the three sites in Guangzhou DWDS were significantly different, despite the similar physicochemical properties of portable water. Some abundant and peculiar bacterial phylotypes were noteworthy, including Methylophilus , Massilia, and Planomicrobium , members of which are rarely found in DWDSs and their roles in DWDS biofilms are still unclear. The diversity of Mycobacterium species in biofilm samples was rather low. Mycobacterium arupense and Mycobacterium gordonae were the primary Mycobacterium species in Guangzhou and Beijing biofilms, respectively, indicating that M. arupense may be more resistant to chloride than M. gordonae.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.180
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations74
Published2012
Admission routes1
Has abstractyes

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